First, the performances of excess return predictor Pre\(_E\)  and adversarial predictor Adv\(_E\)  are shown in Table REF . For LSTM and GRU baselines, they are only used to predict the excess returns. As shown, our ADD method effectively facilitates the ability of predicting excess return with remarkable promotion of Rank IC and IC (e.g., \(0.1480\)  v.s. \(0.1375\)  Rank IC). For adversarial prediction, our method successfully removes most of the excess information from the market encoder Enc\(_M\) , which results a significant decline in the excess return prediction. For example, the Rank IC for ours is \(0.0306\)  while \(0.0627\)  of two-step work [1]}.
